I am hoping someone may be able to provide some guidance as I don't think my analysis is common in the literature.
Variables in data set:
One manager variable: X - is a T-Score. There is NO within group variance in X.
Sub-ordinate variables (2): Y & M - where Y is the outcome variable and M is a proposed moderator variable.
Other variables: Cluster (work teams) and XM (Interaction variable - X*M)
All variables are continuous.
It is theorised that the level 2 variable (X) should predict the Level 1 variable (Y). M should moderate this relationship. Therefore I have a Level 1 moderator for a cross-level relationship. (M is theorised not to predict Y.)
I believe I need to adopt a means as outcome approach. From this there is a significant main effect (i.e. X-->Y). Indeed, this relationship is significant.
However, given that there is no variance at the slopes between teams, I am not sure the approach I should take in regards to modelling the moderation.
I think I need to assess how M moderates the grand slope but I am unsure of this. Would I regress the model of the main relationship X->Y onto XM?
You could use the between part of M to moderate X -> Y on the between level. Either the observed cluster mean of M or the latent part decomposed by Mplus (see UG Chapter 9). But there isn't much going on on Within - you have M and Y but you say M doesn't predict Y. You could estimate their variances on Within.
Or, you could skip within and do a single-level analysis for between units.